Create a count, desc, or shift layer for discrete count based summaries, descriptive statistics summaries, or shift count summaries

This family of functions specifies the type of summary that is to be performed within a layer. count layers are used to create summary counts of some discrete variable. desc layers create summary statistics, and shift layers summaries the counts of different changes in states. See the "details" section below for more information.

Usage

group_count(parent, target_var, by = vars(), where = TRUE, ...)

group_desc(parent, target_var, by = vars(), where = TRUE, ...)

group_shift(parent, target_var, by = vars(), where = TRUE, ...)

Arguments

parent

Required. The parent environment of the layer. This must be the tplyr_table object that the layer is contained within.

target_var

Symbol. Required, The variable name(s) on which the summary is to be performed. Must be a variable within the target dataset. Enter unquoted - i.e. target_var = AEBODSYS. You may also provide multiple variables with vars.

by

A string, a variable name, or a list of variable names supplied using vars

where

Call. Filter logic used to subset the target data when performing a summary.

...

Additional arguments to pass forward

Value

An tplyr_layer environment that is a child of the specified parent. The environment contains the object as listed below.

A tplyr_layer object

Details

Count Layers

Count layers allow you to create summaries based on counting values with a variable. Additionally, this layer allows you to create n (%) summaries where you're also summarizing the proportion of instances a value occurs compared to some denominator. Count layers are also capable of producing counts of nested relationships. For example, if you want to produce counts of an overall outside group, and then the subgroup counts within that group, you can specify the target variable as vars(OutsideVariable, InsideVariable). This allows you to do tables like Adverse Events where you want to see the Preferred Terms within Body Systems, all in one layer. Further control over denominators is available using the function set_denoms_by and distinct counts can be set using set_distinct_by

Descriptive Statistics Layers

Descriptive statistics layers perform summaries on continuous variables. There are a number of summaries built into Tplyr already that you can perform, including n, mean, median, standard deviation, variance, min, max, inter-quartile range, Q1, Q3, and missing value counts. From these available summaries, the default presentation of a descriptive statistic layer will output 'n', 'Mean (SD)', 'Median', 'Q1, Q3', 'Min, Max', and 'Missing'. You can change these summaries using set_format_strings, and you can also add your own summaries using set_custom_summaries. This allows you to implement any additional summary statistics you want presented.

Shift Layers

A shift layer displays an endpoint's 'shift' throughout the duration of the study. It is an abstraction over the count layer, however we have provided an interface that is more efficient and intuitive. Targets are passed as named symbols using dplyr::vars. Generally the baseline is passed with the name 'row' and the shift is passed with the name 'column'. Both counts (n) and percentages (pct) are supported and can be specified with the set_format_strings function. To allow for flexibility when defining percentages, you can define the denominator using the set_denoms_by function. This function takes variable names and uses those to determine the denominator for the counts.

See also

[add_layer, add_layers, tplyr_table, tplyr_layer]

Examples

# Load in pipe
library(magrittr)

t <- tplyr_table(iris, Species) %>%
  add_layer(
    group_desc(target_var=Sepal.Width)
  )

t <- tplyr_table(iris, Species) %>%
  add_layer(
    group_desc(target_var=Sepal.Width)
  )

t <- tplyr_table(mtcars, am) %>%
  add_layer(
    group_shift(vars(row=gear, column=carb), by=cyl)
  )